Life Sciences Reasoning Architect (Remote, Flexible Hours)

Aitrainer; San Francisco (CA); Remote; USD 90,000 - 150,000; Full time

Benefits offered by this job

Cutting-edge AI projects

Flexible hours

Remote work environment

Competitive compensation

Job summary

Turing, based in San Francisco, is seeking Life Sciences Subject Matter Experts to develop advanced scientific reasoning tasks for LifeSciBench. This non-code benchmark focuses on interpreting experiments, data, and literature-style evidence rather than coding.

You will design graduate-level problems grounded in realistic contexts, validate answers with defensible reasoning, and ensure questions are self-contained and reproducible.

Qualifications

• Ph.D., postdoctoral experience, or equivalent advanced research experience in the life sciences.
• Strong expertise in at least one biological discipline.
• Demonstrated ability to interpret scientific experiments, data, and research findings.
• Strong scientific writing skills and attention to precision.

Responsibilities

• Design novel, graduate-level life sciences problems grounded in realistic research contexts.
• Develop clear questions and rigorous reference answers with defensible scientific reasoning.
• Interpret experiments, datasets, figures, sequences, structures, and other biological evidence.
• Validate biological claims, calculations, assumptions, and conclusions.
• Identify ambiguity, alternative interpretations, and insufficiently constrained questions.
• Ensure tasks are self-contained, reproducible where applicable, and clearly documented.
• Incorporate reviewer feedback while maintaining quality and throughput.

Skills

Life sciences knowledge

Scientific reasoning

Scientific writing

Education

Ph.D., postdoc or equivalent advanced research in life sciences

Tools

None

Job description

Turing, based in San Francisco, is seeking Life Sciences Subject Matter Experts to develop advanced scientific reasoning tasks for LifeSciBench. This non-code benchmark focuses on interpreting experiments, data, and literature-style evidence rather than coding.

You will design graduate-level problems grounded in realistic contexts, validate answers with defensible reasoning, and ensure questions are self-contained and reproducible.